Overview

Brought to you by YData

Dataset statistics

Number of variables15
Number of observations1628258
Missing cells103746
Missing cells (%)0.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory352.7 MiB
Average record size in memory227.2 B

Variable types

Numeric10
Categorical4
Text1

Alerts

eval_set has constant value "prior"Constant
aisle has a high cardinality: 134 distinct valuesHigh cardinality
department is highly overall correlated with department_idHigh correlation
department_id is highly overall correlated with departmentHigh correlation
days_since_prior_order has 103746 (6.4%) missing valuesMissing
order_dow has 309011 (19.0%) zerosZeros
days_since_prior_order has 21367 (1.3%) zerosZeros

Reproduction

Analysis started2025-10-12 18:18:59.028778
Analysis finished2025-10-12 18:20:15.914954
Duration1 minute and 16.89 seconds
Software versionydata-profiling vv4.17.0
Download configurationconfig.json

Variables

order_id
Real number (ℝ)

Distinct161752
Distinct (%)9.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1711654.8
Minimum28
Maximum3421083
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.8 MiB
2025-10-12T14:20:16.021953image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum28
5-th percentile167144
Q1861420
median1711303
Q32570465
95-th percentile3249603
Maximum3421083
Range3421055
Interquartile range (IQR)1709045

Descriptive statistics

Standard deviation987490.76
Coefficient of variation (CV)0.57692168
Kurtosis-1.1994225
Mean1711654.8
Median Absolute Deviation (MAD)854109
Skewness-0.0036100055
Sum2.7870156 × 1012
Variance9.75138 × 1011
MonotonicityIncreasing
2025-10-12T14:20:16.220947image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
306507786
 
< 0.1%
262142876
 
< 0.1%
52129172
 
< 0.1%
264080071
 
< 0.1%
287709070
 
< 0.1%
147713968
 
< 0.1%
172354967
 
< 0.1%
100760964
 
< 0.1%
320560063
 
< 0.1%
309793363
 
< 0.1%
Other values (161742)1627558
> 99.9%
ValueCountFrequency (%)
2816
< 0.1%
488
 
< 0.1%
547
 
< 0.1%
678
 
< 0.1%
7110
< 0.1%
10921
< 0.1%
1222
 
< 0.1%
1386
 
< 0.1%
1514
 
< 0.1%
1565
 
< 0.1%
ValueCountFrequency (%)
342108310
< 0.1%
34210809
< 0.1%
34210666
 
< 0.1%
34209888
< 0.1%
342096810
< 0.1%
342093812
< 0.1%
342091815
< 0.1%
34209086
 
< 0.1%
342090112
< 0.1%
342089710
< 0.1%

product_id
Real number (ℝ)

Distinct35600
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25645.597
Minimum1
Maximum49688
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.8 MiB
2025-10-12T14:20:16.370067image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3399
Q113598
median25464
Q338100
95-th percentile47601
Maximum49688
Range49687
Interquartile range (IQR)24502

Descriptive statistics

Standard deviation14092.605
Coefficient of variation (CV)0.54951364
Kurtosis-1.140182
Mean25645.597
Median Absolute Deviation (MAD)12215
Skewness-0.026094877
Sum4.1757649 × 1010
Variance1.9860153 × 108
MonotonicityNot monotonic
2025-10-12T14:20:16.515193image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2485223082
 
1.4%
1317619056
 
1.2%
2113713445
 
0.8%
2190312152
 
0.7%
4720910874
 
0.7%
477669073
 
0.6%
476267616
 
0.5%
262097349
 
0.5%
278457197
 
0.4%
167977075
 
0.4%
Other values (35590)1511339
92.8%
ValueCountFrequency (%)
171
< 0.1%
22
 
< 0.1%
36
 
< 0.1%
413
 
< 0.1%
52
 
< 0.1%
81
 
< 0.1%
98
 
< 0.1%
10134
< 0.1%
118
 
< 0.1%
1212
 
< 0.1%
ValueCountFrequency (%)
496882
 
< 0.1%
496862
 
< 0.1%
496856
 
< 0.1%
496835192
0.3%
496821
 
< 0.1%
4968062
 
< 0.1%
496795
 
< 0.1%
4967810
 
< 0.1%
496779
 
< 0.1%
496764
 
< 0.1%

add_to_cart_order
Real number (ℝ)

Distinct86
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.2750479
Minimum1
Maximum86
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.8 MiB
2025-10-12T14:20:16.658769image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q311
95-th percentile22
Maximum86
Range85
Interquartile range (IQR)8

Descriptive statistics

Standard deviation6.9281293
Coefficient of variation (CV)0.83723131
Kurtosis3.6996824
Mean8.2750479
Median Absolute Deviation (MAD)4
Skewness1.6209974
Sum13473913
Variance47.998975
MonotonicityNot monotonic
2025-10-12T14:20:16.798551image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1161752
 
9.9%
2153881
 
9.5%
3144405
 
8.9%
4133991
 
8.2%
5123003
 
7.6%
6111455
 
6.8%
799893
 
6.1%
888888
 
5.5%
978593
 
4.8%
1069340
 
4.3%
Other values (76)463057
28.4%
ValueCountFrequency (%)
1161752
9.9%
2153881
9.5%
3144405
8.9%
4133991
8.2%
5123003
7.6%
6111455
6.8%
799893
6.1%
888888
5.5%
978593
4.8%
1069340
4.3%
ValueCountFrequency (%)
861
< 0.1%
851
< 0.1%
841
< 0.1%
831
< 0.1%
821
< 0.1%
811
< 0.1%
801
< 0.1%
791
< 0.1%
781
< 0.1%
771
< 0.1%

reordered
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size90.1 MiB
1
958236 
0
670022 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1628258
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1958236
58.9%
0670022
41.1%

Length

2025-10-12T14:20:16.922538image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-12T14:20:17.030195image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
1958236
58.9%
0670022
41.1%

Most occurring characters

ValueCountFrequency (%)
1958236
58.9%
0670022
41.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1628258
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1958236
58.9%
0670022
41.1%

Most occurring scripts

ValueCountFrequency (%)
Common1628258
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1958236
58.9%
0670022
41.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII1628258
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1958236
58.9%
0670022
41.1%
Distinct35600
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size127.6 MiB
2025-10-12T14:20:17.383613image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length159
Median length120
Mean length24.95476
Min length3

Characters and Unicode

Total characters40632787
Distinct characters105
Distinct categories14 ?
Distinct scripts3 ?
Distinct blocks7 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6761 ?
Unique (%)0.4%

Sample

1st rowSalted Butter
2nd rowCream Cheese
3rd rowAir Chilled Organic Boneless Skinless Chicken Breasts
4th rowOrganic D'Anjou Pears
5th rowCultured Low Fat Buttermilk
ValueCountFrequency (%)
organic518491
 
8.4%
100020
 
1.6%
milk86989
 
1.4%
cheese71582
 
1.2%
yogurt66273
 
1.1%
whole62735
 
1.0%
free58486
 
0.9%
water50567
 
0.8%
baby49910
 
0.8%
original48536
 
0.8%
Other values (9606)5070524
82.0%
2025-10-12T14:20:17.900844image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4564521
 
11.2%
e3814750
 
9.4%
a3465999
 
8.5%
r2868253
 
7.1%
i2403362
 
5.9%
n2229947
 
5.5%
o1783377
 
4.4%
l1679957
 
4.1%
t1670456
 
4.1%
s1355246
 
3.3%
Other values (95)14796919
36.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter29544165
72.7%
Uppercase Letter5997347
 
14.8%
Space Separator4564798
 
11.2%
Other Punctuation266245
 
0.7%
Decimal Number190364
 
0.5%
Dash Punctuation47240
 
0.1%
Close Punctuation7826
 
< 0.1%
Open Punctuation7826
 
< 0.1%
Math Symbol3720
 
< 0.1%
Other Symbol3194
 
< 0.1%
Other values (4)62
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e3814750
12.9%
a3465999
11.7%
r2868253
9.7%
i2403362
 
8.1%
n2229947
 
7.5%
o1783377
 
6.0%
l1679957
 
5.7%
t1670456
 
5.7%
s1355246
 
4.6%
c1318540
 
4.5%
Other values (26)6954278
23.5%
Uppercase Letter
ValueCountFrequency (%)
C731615
12.2%
O684910
11.4%
S677128
11.3%
B615469
 
10.3%
P379916
 
6.3%
F297783
 
5.0%
M288947
 
4.8%
G282360
 
4.7%
A243320
 
4.1%
R237773
 
4.0%
Other values (16)1558126
26.0%
Other Punctuation
ValueCountFrequency (%)
&93682
35.2%
,70277
26.4%
%63226
23.7%
'15156
 
5.7%
/9318
 
3.5%
!4733
 
1.8%
.4565
 
1.7%
:1909
 
0.7%
\1353
 
0.5%
"1353
 
0.5%
Other values (5)673
 
0.3%
Decimal Number
ValueCountFrequency (%)
066384
34.9%
142352
22.2%
236411
19.1%
312944
 
6.8%
47963
 
4.2%
57632
 
4.0%
95711
 
3.0%
84173
 
2.2%
73453
 
1.8%
63341
 
1.8%
Other Symbol
ValueCountFrequency (%)
®1966
61.6%
1223
38.3%
4
 
0.1%
°1
 
< 0.1%
Dash Punctuation
ValueCountFrequency (%)
-47087
99.7%
145
 
0.3%
8
 
< 0.1%
Space Separator
ValueCountFrequency (%)
4564521
> 99.9%
 277
 
< 0.1%
Math Symbol
ValueCountFrequency (%)
+3505
94.2%
=215
 
5.8%
Modifier Symbol
ValueCountFrequency (%)
˚4
57.1%
´3
42.9%
Close Punctuation
ValueCountFrequency (%)
)7826
100.0%
Open Punctuation
ValueCountFrequency (%)
(7826
100.0%
Currency Symbol
ValueCountFrequency (%)
$24
100.0%
Final Punctuation
ValueCountFrequency (%)
19
100.0%
Control
ValueCountFrequency (%)
12
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin35541444
87.5%
Common5091275
 
12.5%
Cyrillic68
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e3814750
 
10.7%
a3465999
 
9.8%
r2868253
 
8.1%
i2403362
 
6.8%
n2229947
 
6.3%
o1783377
 
5.0%
l1679957
 
4.7%
t1670456
 
4.7%
s1355246
 
3.8%
c1318540
 
3.7%
Other values (51)12951557
36.4%
Common
ValueCountFrequency (%)
4564521
89.7%
&93682
 
1.8%
,70277
 
1.4%
066384
 
1.3%
%63226
 
1.2%
-47087
 
0.9%
142352
 
0.8%
236411
 
0.7%
'15156
 
0.3%
312944
 
0.3%
Other values (33)79235
 
1.6%
Cyrillic
ValueCountFrequency (%)
е68
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII40627936
> 99.9%
None3380
 
< 0.1%
Letterlike Symbols1223
 
< 0.1%
Punctuation172
 
< 0.1%
Cyrillic68
 
< 0.1%
Modifier Letters4
 
< 0.1%
Specials4
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4564521
 
11.2%
e3814750
 
9.4%
a3465999
 
8.5%
r2868253
 
7.1%
i2403362
 
5.9%
n2229947
 
5.5%
o1783377
 
4.4%
l1679957
 
4.1%
t1670456
 
4.1%
s1355246
 
3.3%
Other values (74)14792068
36.4%
None
ValueCountFrequency (%)
®1966
58.2%
é710
 
21.0%
 277
 
8.2%
ñ246
 
7.3%
è92
 
2.7%
í48
 
1.4%
12
 
0.4%
ü8
 
0.2%
â6
 
0.2%
ô6
 
0.2%
Other values (4)9
 
0.3%
Letterlike Symbols
ValueCountFrequency (%)
1223
100.0%
Punctuation
ValueCountFrequency (%)
145
84.3%
19
 
11.0%
8
 
4.7%
Cyrillic
ValueCountFrequency (%)
е68
100.0%
Modifier Letters
ValueCountFrequency (%)
˚4
100.0%
Specials
ValueCountFrequency (%)
4
100.0%

aisle_id
Real number (ℝ)

Distinct134
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean71.227631
Minimum1
Maximum134
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.8 MiB
2025-10-12T14:20:18.059275image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile16
Q131
median83
Q3107
95-th percentile123
Maximum134
Range133
Interquartile range (IQR)76

Descriptive statistics

Standard deviation38.151598
Coefficient of variation (CV)0.53562918
Kurtosis-1.3250255
Mean71.227631
Median Absolute Deviation (MAD)33
Skewness-0.16663328
Sum1.1597696 × 108
Variance1455.5444
MonotonicityNot monotonic
2025-10-12T14:20:18.210221image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24182972
 
11.2%
83173830
 
10.7%
12389048
 
5.5%
12071129
 
4.4%
2148244
 
3.0%
8444769
 
2.7%
11543346
 
2.7%
10735820
 
2.2%
9132037
 
2.0%
11229796
 
1.8%
Other values (124)877267
53.9%
ValueCountFrequency (%)
13331
 
0.2%
24200
 
0.3%
321859
1.3%
49632
0.6%
53159
 
0.2%
61939
 
0.1%
71640
 
0.1%
81749
 
0.1%
911078
0.7%
10429
 
< 0.1%
ValueCountFrequency (%)
134550
 
< 0.1%
133975
 
0.1%
132344
 
< 0.1%
13113589
0.8%
1307931
0.5%
1299163
0.6%
1289792
0.6%
1271956
 
0.1%
126946
 
0.1%
1251722
 
0.1%

department_id
Real number (ℝ)

High correlation 

Distinct21
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.8816557
Minimum1
Maximum21
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.8 MiB
2025-10-12T14:20:18.341224image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median9
Q316
95-th percentile19
Maximum21
Range20
Interquartile range (IQR)12

Descriptive statistics

Standard deviation6.2720808
Coefficient of variation (CV)0.63471963
Kurtosis-1.556463
Mean9.8816557
Median Absolute Deviation (MAD)5
Skewness0.15978088
Sum16089885
Variance39.338998
MonotonicityNot monotonic
2025-10-12T14:20:18.467487image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
4479501
29.4%
16270170
16.6%
19141984
 
8.7%
7133421
 
8.2%
1112718
 
6.9%
1395048
 
5.8%
359411
 
3.6%
1553922
 
3.3%
2052533
 
3.2%
943511
 
2.7%
Other values (11)186039
 
11.4%
ValueCountFrequency (%)
1112718
 
6.9%
21939
 
0.1%
359411
 
3.6%
4479501
29.4%
57448
 
0.5%
614351
 
0.9%
7133421
 
8.2%
85104
 
0.3%
943511
 
2.7%
101530
 
0.1%
ValueCountFrequency (%)
213435
 
0.2%
2052533
 
3.2%
19141984
8.7%
1820834
 
1.3%
1736529
 
2.2%
16270170
16.6%
1553922
 
3.3%
1436173
 
2.2%
1395048
 
5.8%
1236280
 
2.2%

aisle
Categorical

High cardinality 

Distinct134
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size15.5 MiB
fresh fruits
182972 
fresh vegetables
173830 
packaged vegetables fruits
 
89048
yogurt
 
71129
packaged cheese
 
48244
Other values (129)
1063035 

Length

Max length29
Median length23
Mean length14.451632
Min length3

Characters and Unicode

Total characters23530985
Distinct characters26
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowbutter
2nd rowother creams cheeses
3rd rowpoultry counter
4th rowfresh fruits
5th rowmilk

Common Values

ValueCountFrequency (%)
fresh fruits182972
 
11.2%
fresh vegetables173830
 
10.7%
packaged vegetables fruits89048
 
5.5%
yogurt71129
 
4.4%
packaged cheese48244
 
3.0%
milk44769
 
2.7%
water seltzer sparkling water43346
 
2.7%
chips pretzels35820
 
2.2%
soy lactosefree32037
 
2.0%
bread29796
 
1.8%
Other values (124)877267
53.9%

Length

2025-10-12T14:20:18.612485image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
fresh396104
 
11.4%
vegetables278597
 
8.0%
fruits272837
 
7.9%
packaged160237
 
4.6%
frozen88137
 
2.5%
water86692
 
2.5%
yogurt71129
 
2.1%
ice49715
 
1.4%
cheese48244
 
1.4%
milk44769
 
1.3%
Other values (194)1964439
56.8%

Most occurring characters

ValueCountFrequency (%)
e3369157
14.3%
s2230435
 
9.5%
r1993943
 
8.5%
a1848210
 
7.9%
1832642
 
7.8%
t1383598
 
5.9%
f1014726
 
4.3%
i938425
 
4.0%
o885014
 
3.8%
g877805
 
3.7%
Other values (16)7157030
30.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter21698343
92.2%
Space Separator1832642
 
7.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e3369157
15.5%
s2230435
 
10.3%
r1993943
 
9.2%
a1848210
 
8.5%
t1383598
 
6.4%
f1014726
 
4.7%
i938425
 
4.3%
o885014
 
4.1%
g877805
 
4.0%
c836378
 
3.9%
Other values (15)6320652
29.1%
Space Separator
ValueCountFrequency (%)
1832642
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin21698343
92.2%
Common1832642
 
7.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
e3369157
15.5%
s2230435
 
10.3%
r1993943
 
9.2%
a1848210
 
8.5%
t1383598
 
6.4%
f1014726
 
4.7%
i938425
 
4.3%
o885014
 
4.1%
g877805
 
4.0%
c836378
 
3.9%
Other values (15)6320652
29.1%
Common
ValueCountFrequency (%)
1832642
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII23530985
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e3369157
14.3%
s2230435
 
9.5%
r1993943
 
8.5%
a1848210
 
7.9%
1832642
 
7.8%
t1383598
 
5.9%
f1014726
 
4.3%
i938425
 
4.0%
o885014
 
3.8%
g877805
 
3.7%
Other values (16)7157030
30.4%

department
Categorical

High correlation 

Distinct21
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size14.0 MiB
produce
479501 
dairy eggs
270170 
snacks
141984 
beverages
133421 
frozen
112718 
Other values (16)
490464 

Length

Max length15
Median length13
Mean length8.0017344
Min length4

Characters and Unicode

Total characters13028888
Distinct characters23
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowdairy eggs
2nd rowdairy eggs
3rd rowmeat seafood
4th rowproduce
5th rowdairy eggs

Common Values

ValueCountFrequency (%)
produce479501
29.4%
dairy eggs270170
16.6%
snacks141984
 
8.7%
beverages133421
 
8.2%
frozen112718
 
6.9%
pantry95048
 
5.8%
bakery59411
 
3.6%
canned goods53922
 
3.3%
deli52533
 
3.2%
dry goods pasta43511
 
2.7%
Other values (11)186039
 
11.4%

Length

2025-10-12T14:20:18.740491image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
produce479501
22.9%
dairy270170
12.9%
eggs270170
12.9%
snacks141984
 
6.8%
beverages133421
 
6.4%
frozen112718
 
5.4%
goods97433
 
4.6%
pantry95048
 
4.5%
bakery59411
 
2.8%
canned53922
 
2.6%
Other values (16)384290
18.3%

Most occurring characters

ValueCountFrequency (%)
e1660840
12.7%
r1291075
 
9.9%
a1087700
 
8.3%
d1069879
 
8.2%
s992709
 
7.6%
o986305
 
7.6%
g774629
 
5.9%
c705271
 
5.4%
p645580
 
5.0%
n526498
 
4.0%
Other values (13)3288402
25.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter12559078
96.4%
Space Separator469810
 
3.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e1660840
13.2%
r1291075
10.3%
a1087700
 
8.7%
d1069879
 
8.5%
s992709
 
7.9%
o986305
 
7.9%
g774629
 
6.2%
c705271
 
5.6%
p645580
 
5.1%
n526498
 
4.2%
Other values (12)2818592
22.4%
Space Separator
ValueCountFrequency (%)
469810
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin12559078
96.4%
Common469810
 
3.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e1660840
13.2%
r1291075
10.3%
a1087700
 
8.7%
d1069879
 
8.5%
s992709
 
7.9%
o986305
 
7.9%
g774629
 
6.2%
c705271
 
5.6%
p645580
 
5.1%
n526498
 
4.2%
Other values (12)2818592
22.4%
Common
ValueCountFrequency (%)
469810
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII13028888
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e1660840
12.7%
r1291075
 
9.9%
a1087700
 
8.3%
d1069879
 
8.2%
s992709
 
7.6%
o986305
 
7.6%
g774629
 
5.9%
c705271
 
5.4%
p645580
 
5.0%
n526498
 
4.0%
Other values (13)3288402
25.2%

user_id
Real number (ℝ)

Distinct10310
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean103260.41
Minimum5
Maximum206186
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.8 MiB
2025-10-12T14:20:18.875485image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile11859
Q152889
median101343
Q3155002
95-th percentile195555
Maximum206186
Range206181
Interquartile range (IQR)102113

Descriptive statistics

Standard deviation59044.277
Coefficient of variation (CV)0.57179977
Kurtosis-1.1996034
Mean103260.41
Median Absolute Deviation (MAD)51022
Skewness0.021337609
Sum1.6813458 × 1011
Variance3.4862266 × 109
MonotonicityNot monotonic
2025-10-12T14:20:19.014485image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1824012907
 
0.2%
1474802004
 
0.1%
959671992
 
0.1%
1320221920
 
0.1%
1513781886
 
0.1%
1717711862
 
0.1%
1467701809
 
0.1%
290411776
 
0.1%
131311600
 
0.1%
539281587
 
0.1%
Other values (10300)1608915
98.8%
ValueCountFrequency (%)
537
 
< 0.1%
7206
< 0.1%
1381
 
< 0.1%
3432
 
< 0.1%
71437
< 0.1%
10259
 
< 0.1%
10641
 
< 0.1%
118165
 
< 0.1%
12479
 
< 0.1%
12964
 
< 0.1%
ValueCountFrequency (%)
20618632
 
< 0.1%
206165559
< 0.1%
206138117
 
< 0.1%
20613321
 
< 0.1%
206126524
< 0.1%
206084110
 
< 0.1%
20607618
 
< 0.1%
206054156
 
< 0.1%
206044158
 
< 0.1%
206043361
< 0.1%

eval_set
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size96.3 MiB
prior
1628258 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters8141290
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowprior
2nd rowprior
3rd rowprior
4th rowprior
5th rowprior

Common Values

ValueCountFrequency (%)
prior1628258
100.0%

Length

2025-10-12T14:20:19.156260image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-12T14:20:19.254895image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
prior1628258
100.0%

Most occurring characters

ValueCountFrequency (%)
r3256516
40.0%
p1628258
20.0%
i1628258
20.0%
o1628258
20.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter8141290
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r3256516
40.0%
p1628258
20.0%
i1628258
20.0%
o1628258
20.0%

Most occurring scripts

ValueCountFrequency (%)
Latin8141290
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r3256516
40.0%
p1628258
20.0%
i1628258
20.0%
o1628258
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII8141290
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r3256516
40.0%
p1628258
20.0%
i1628258
20.0%
o1628258
20.0%

order_number
Real number (ℝ)

Distinct99
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.261568
Minimum1
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.8 MiB
2025-10-12T14:20:19.375321image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q15
median11
Q324
95-th percentile55
Maximum99
Range98
Interquartile range (IQR)19

Descriptive statistics

Standard deviation17.698826
Coefficient of variation (CV)1.0253313
Kurtosis3.2365932
Mean17.261568
Median Absolute Deviation (MAD)8
Skewness1.7618986
Sum28106287
Variance313.24844
MonotonicityNot monotonic
2025-10-12T14:20:19.516321image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1103746
 
6.4%
3102230
 
6.3%
2102105
 
6.3%
490874
 
5.6%
581424
 
5.0%
673836
 
4.5%
766540
 
4.1%
860463
 
3.7%
955667
 
3.4%
1052192
 
3.2%
Other values (89)839181
51.5%
ValueCountFrequency (%)
1103746
6.4%
2102105
6.3%
3102230
6.3%
490874
5.6%
581424
5.0%
673836
4.5%
766540
4.1%
860463
3.7%
955667
3.4%
1052192
3.2%
ValueCountFrequency (%)
99584
< 0.1%
98623
< 0.1%
97681
< 0.1%
96794
< 0.1%
95686
< 0.1%
94795
< 0.1%
93892
0.1%
92849
0.1%
91817
0.1%
90909
0.1%

order_dow
Real number (ℝ)

Zeros 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.7427361
Minimum0
Maximum6
Zeros309011
Zeros (%)19.0%
Negative0
Negative (%)0.0%
Memory size24.8 MiB
2025-10-12T14:20:19.625318image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q35
95-th percentile6
Maximum6
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.0874903
Coefficient of variation (CV)0.76109775
Kurtosis-1.3349633
Mean2.7427361
Median Absolute Deviation (MAD)2
Skewness0.17624328
Sum4465882
Variance4.3576157
MonotonicityNot monotonic
2025-10-12T14:20:19.726412image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0309011
19.0%
1287284
17.6%
6224543
13.8%
5212734
13.1%
2208707
12.8%
3193660
11.9%
4192319
11.8%
ValueCountFrequency (%)
0309011
19.0%
1287284
17.6%
2208707
12.8%
3193660
11.9%
4192319
11.8%
5212734
13.1%
6224543
13.8%
ValueCountFrequency (%)
6224543
13.8%
5212734
13.1%
4192319
11.8%
3193660
11.9%
2208707
12.8%
1287284
17.6%
0309011
19.0%

order_hour_of_day
Real number (ℝ)

Distinct24
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.412536
Minimum0
Maximum23
Zeros11147
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size24.8 MiB
2025-10-12T14:20:19.837409image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7
Q110
median13
Q316
95-th percentile21
Maximum23
Range23
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.2421835
Coefficient of variation (CV)0.31628497
Kurtosis0.0033815488
Mean13.412536
Median Absolute Deviation (MAD)3
Skewness-0.041912801
Sum21839069
Variance17.996121
MonotonicityNot monotonic
2025-10-12T14:20:19.954930image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
11140977
 
8.7%
10140359
 
8.6%
14137369
 
8.4%
15133199
 
8.2%
13132740
 
8.2%
12132071
 
8.1%
16125882
 
7.7%
9122260
 
7.5%
17103512
 
6.4%
884685
 
5.2%
Other values (14)375204
23.0%
ValueCountFrequency (%)
011147
 
0.7%
15762
 
0.4%
23755
 
0.2%
32530
 
0.2%
42362
 
0.1%
54718
 
0.3%
614574
 
0.9%
744939
 
2.8%
884685
5.2%
9122260
7.5%
ValueCountFrequency (%)
2319740
 
1.2%
2232215
 
2.0%
2139597
 
2.4%
2049280
 
3.0%
1963139
3.9%
1881446
5.0%
17103512
6.4%
16125882
7.7%
15133199
8.2%
14137369
8.4%

days_since_prior_order
Real number (ℝ)

Missing  Zeros 

Distinct31
Distinct (%)< 0.1%
Missing103746
Missing (%)6.4%
Infinite0
Infinite (%)0.0%
Mean11.077978
Minimum0
Maximum30
Zeros21367
Zeros (%)1.3%
Negative0
Negative (%)0.0%
Memory size24.8 MiB
2025-10-12T14:20:20.076685image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q15
median8
Q315
95-th percentile30
Maximum30
Range30
Interquartile range (IQR)10

Descriptive statistics

Standard deviation8.769521
Coefficient of variation (CV)0.79161745
Kurtosis-0.058519759
Mean11.077978
Median Absolute Deviation (MAD)4
Skewness1.0561316
Sum16888511
Variance76.904498
MonotonicityNot monotonic
2025-10-12T14:20:20.205224image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
7170442
 
10.5%
30153514
 
9.4%
6125498
 
7.7%
5107719
 
6.6%
4106087
 
6.5%
395801
 
5.9%
895159
 
5.8%
275652
 
4.6%
960567
 
3.7%
1451153
 
3.1%
Other values (21)482920
29.7%
(Missing)103746
 
6.4%
ValueCountFrequency (%)
021367
 
1.3%
148220
 
3.0%
275652
4.6%
395801
5.9%
4106087
6.5%
5107719
6.6%
6125498
7.7%
7170442
10.5%
895159
5.8%
960567
 
3.7%
ValueCountFrequency (%)
30153514
9.4%
298702
 
0.5%
2812606
 
0.8%
279750
 
0.6%
268805
 
0.5%
259091
 
0.6%
249811
 
0.6%
2310895
 
0.7%
2215874
 
1.0%
2122129
 
1.4%

Interactions

2025-10-12T14:20:07.215788image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T14:19:33.403922image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T14:19:37.519542image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T14:19:41.264288image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T14:19:44.927295image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T14:19:48.675534image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T14:19:52.401479image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T14:19:56.064364image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T14:19:59.630320image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T14:20:03.641975image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T14:20:07.592933image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T14:19:33.789759image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T14:19:37.929150image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T14:19:41.623535image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T14:19:45.296308image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T14:19:49.052533image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T14:19:52.777062image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T14:19:56.434787image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T14:20:00.006322image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T14:20:04.018992image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T14:20:07.954912image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T14:19:34.186939image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T14:19:38.296494image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T14:19:41.966419image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T14:19:45.669308image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T14:19:49.410763image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T14:19:53.140067image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T14:19:56.795795image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T14:20:00.397316image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T14:20:04.381496image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T14:20:08.316915image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T14:19:34.547940image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T14:19:38.653766image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T14:19:42.325655image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T14:19:46.022919image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T14:19:49.773686image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T14:19:53.498067image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T14:19:57.154786image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T14:20:00.770350image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T14:20:04.741465image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T14:20:08.702927image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T14:19:35.122966image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T14:19:39.020347image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T14:19:42.678218image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T14:19:46.396902image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T14:19:50.135428image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T14:19:53.958090image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T14:19:57.520786image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T14:20:01.139943image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T14:20:05.111980image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T14:20:09.086310image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T14:19:35.552950image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T14:19:39.407740image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T14:19:43.071458image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T14:19:46.807441image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T14:19:50.529402image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T14:19:54.302564image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T14:19:57.875587image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T14:20:01.881790image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T14:20:05.460347image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T14:20:09.444319image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T14:19:35.966943image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T14:19:39.781908image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T14:19:43.432741image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T14:19:47.185723image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T14:19:50.901360image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T14:19:54.642568image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T14:19:58.214047image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T14:20:02.229783image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T14:20:05.805298image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T14:20:09.807715image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T14:19:36.394941image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T14:19:40.160321image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T14:19:43.826749image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T14:19:47.558517image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T14:19:51.283423image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T14:19:54.984558image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T14:19:58.560047image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T14:20:02.564946image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T14:20:06.157964image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T14:20:10.188473image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T14:19:36.783619image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T14:19:40.540840image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T14:19:44.203521image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T14:19:47.941325image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T14:19:51.657426image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T14:19:55.337558image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T14:19:58.906048image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T14:20:02.916948image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T14:20:06.498963image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T14:20:10.535535image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T14:19:37.153605image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T14:19:40.909698image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T14:19:44.570219image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T14:19:48.319573image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T14:19:52.035484image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T14:19:55.691366image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T14:19:59.263320image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T14:20:03.275945image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T14:20:06.854771image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Correlations

2025-10-12T14:20:20.310214image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
add_to_cart_orderaisle_iddays_since_prior_orderdepartmentdepartment_idorder_doworder_hour_of_dayorder_idorder_numberproduct_idreordereduser_id
add_to_cart_order1.0000.0030.0800.0390.009-0.012-0.019-0.000-0.0020.0080.1280.011
aisle_id0.0031.0000.0040.4390.024-0.001-0.0030.0000.0060.0060.0880.001
days_since_prior_order0.0800.0041.0000.0190.001-0.041-0.007-0.003-0.384-0.0020.1370.016
department0.0390.4390.0191.0001.0000.0240.0150.0030.0180.0750.1980.018
department_id0.0090.0240.0011.0001.0000.008-0.0100.0010.002-0.0230.1590.001
order_dow-0.012-0.001-0.0410.0240.0081.0000.007-0.0010.010-0.0020.016-0.009
order_hour_of_day-0.019-0.003-0.0070.015-0.0100.0071.0000.001-0.040-0.0000.0360.007
order_id-0.0000.000-0.0030.0030.001-0.0010.0011.0000.0020.0000.003-0.001
order_number-0.0020.006-0.3840.0180.0020.010-0.0400.0021.0000.0030.342-0.012
product_id0.0080.006-0.0020.075-0.023-0.002-0.0000.0000.0031.0000.040-0.002
reordered0.1280.0880.1370.1980.1590.0160.0360.0030.3420.0401.0000.017
user_id0.0110.0010.0160.0180.001-0.0090.007-0.001-0.012-0.0020.0171.000

Missing values

2025-10-12T14:20:10.810525image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2025-10-12T14:20:12.243525image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

order_idproduct_idadd_to_cart_orderreorderedproduct_nameaisle_iddepartment_idaisledepartmentuser_ideval_setorder_numberorder_doworder_hour_of_daydays_since_prior_order
268283510810Salted Butter3616butterdairy eggs98256prior293136.0
269284059321Cream Cheese10816other creams cheesesdairy eggs98256prior293136.0
270281746130Air Chilled Organic Boneless Skinless Chicken Breasts3512poultry countermeat seafood98256prior293136.0
271282282541Organic D'Anjou Pears244fresh fruitsproduce98256prior293136.0
272282525651Cultured Low Fat Buttermilk8416milkdairy eggs98256prior293136.0
273284762661Large Lemon244fresh fruitsproduce98256prior293136.0
274283722071Organic Strawberry Fruit Spread8813spreadspantry98256prior293136.0
275281298081Large Greenhouse Tomato834fresh vegetablesproduce98256prior293136.0
276282754891Original Semisoft Cheese2116packaged cheesedairy eggs98256prior293136.0
2772845504101Whole Organic Omega 3 Milk8416milkdairy eggs98256prior293136.0
order_idproduct_idadd_to_cart_orderreorderedproduct_nameaisle_iddepartment_idaisledepartmentuser_ideval_setorder_numberorder_doworder_hour_of_daydays_since_prior_order
324344793421083785410Freeze Dried Mango Slices11719nuts seeds dried fruitsnacks25247prior242621.0
3243448034210834530920Purple Carrot & blueberry Puffs9218baby food formulababies25247prior242621.0
3243448134210832116230Organic Mixed Berry Yogurt & Fruit Snack9218baby food formulababies25247prior242621.0
3243448234210831817641Organic Strawberry Yogurt & Fruit Snack9218baby food formulababies25247prior242621.0
3243448334210833521150Organic Strawberry & Mango Dried Tiny Fruits9218baby food formulababies25247prior242621.0
3243448434210833967861Free & Clear Natural Dishwasher Detergent7417dish detergentshousehold25247prior242621.0
3243448534210831135270Organic Mini Sandwich Crackers Peanut Butter7819crackerssnacks25247prior242621.0
324344863421083460080All Natural French Toast Sticks521frozen breakfastfrozen25247prior242621.0
3243448734210832485291Banana244fresh fruitsproduce25247prior242621.0
3243448834210835020101Organic Sweet & Salty Peanut Pretzel Granola Bars319energy granola barssnacks25247prior242621.0